Descriptive Statistics and Binding Constraint in Kenya (2018)

In the past weeks, the World Bank released new Enterprise Survey Data for 2018. This new data follows the same format as the previous version with additional questions focusing on mobile money. While a previous version of this article was done with 2013 data, I believe updating it to best reflect current situation is necessary. What follows is in a similar vein to my previous paper on the topic. If you read the previous paper, feel free to skip to the updated figures and subsequent analysis.

According
to the World Bank, Kenya is classified as a lower-middle-income economy with a
gross national income of $1,460 (USD). As such, it is ideally situated to
leverage the work of nonprofits to bring about positive economic growth. Unfortunately,
nonprofits often lack the financial and personnel resources to determine
analytically where those resources are best allocated. This article aims to
alleviate that problem for nonprofits in Kenya by developing a general
framework of the binding constraints affecting Kenyan businesses. Practical
examples of binding constraints that may be present in Kenya include the
manufacturing sector where the resources for expansion may be available, but
the electricity infrastructure is lacking. Finance has also historically been a
constraint for Kenyan business although mobile money has dramatically changed
this in recent years. New developments (ongoing since 2007) have resulted in
two thirds of adults in Kenya having access to mobile money (https://www.economist.com/the-economist-explains/2015/03/02/why-does-kenya-lead-the-world-in-mobile-money).
It is my hope that this article and its
contents can be used as a resource to assist both nonprofits and businesses in
Kenya as they attempt to grow. Additionally, the results of this article may be
of use to nonprofits aimed at funding startups as it provides a general guide
to focus their efforts.

The article follows the following
structure: a brief overview of the data used, general descriptive statistics including
graphical representations of findings, a brief comparison between 2013 and 2018
figures, and a reprise of the findings. Additionally, this article aims to
provide insights into how this information can be used wherever possible.

Data

This study uses World Bank
Enterprise Survey data on 1001 domestic Kenyan businesses. Firms are separated
into three categories: small, medium, and large. Small firms are comprised of
5-19 employees. Medium firms are comprised of 20-99 workers. Large firms have
100+ employees. The World Bank Enterprise Survey data excludes firms with less
than 5 employees along with the informal sector. It is intended to be
representative of the private sector economy excluding agriculture.

The data includes rankings for 16
different obstacles on an ordinal scale. We use the framework proposed by
Hausmann, Rodrik, and Velasco (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.61.4218&rep=rep1&type=pdf) to identify binding constraints and
classify the obstacles accordingly. Figure 17at
the end of this article details a flowchart summarizing the HRV
framework. Table 2 in the supplemental pdf details my method of classifying the
various obstacles within the HRV framework.

Figure 1 (Hausmann, Rodrik & Velasco, 2005)

Descriptive Statistics

Figure 2 shows the proportion of
Kenyan firms that identify issues as either major or very severe obstacles.
From the figure it is clear that “Practices
of Competitors in the Informal Sector”, “Corruption”,
“Political Instability”,
and “Tax Rates”
comprise the most significant obstacles for firms in Kenya. “Labor
Regulations”, “Inadequately
Educated Workforce”, “Courts”,
and “Crime, Theft, and Disorder”
are not as significant. It is interesting to note that the composition of this
figure is dramatically different than it was with the 2013 data. This suggests
that the Kenyan business landscape is fairly dynamic across time.

Figure 2:

Our dataset is comprised of three different sizes of firms and it would be naïve to assume that they all face identical constraints. What constitutes a binding constraint for a small firm could very well be partially or completely alleviated by a firm’s size. Table 1 shows the breakdown of the top five most identified obstacles for firms in Kenya according to size. From this table it is clear that Practices of Competition in the Informal Sector, Corruption, Political Instability, and Tax Rates are similar obstacles for all three firm sizes while Access to Finance appears to have different impacts on firms according to their size. While there are a number of obstacles that seem fairly consistent across firm size, it still seems as if firm size has an association with constraints. Note that access to finance appears among the biggest problems for small firms while seeming to become a smaller issues as firm size increases. This could be a problem area where nonprofits could help by focusing on microloans in Kenya.It demonstrates an unmet demand for finance from smaller firms. One firm that is already focusing their efforts on microloans is Kiva Zip[1]p. Started in 2005, Kiva Zip provides loans as small as $50 dollars to businesses in Kenya’s capital city Nairobi. These loans are devoid of both interest and fees with 100% of the process being able to be conducted via a simple mobile phone. Initial results of Kiva Zip have been very promising with a 90% repayment rate and a tripling of average monthly income among recipients (UNHCR 2018). The fact that access to finance is an obstacle at all runs against the common story of Kenya leading the world in access to mobile money. Subsequent posts will attempt to reconcile the impact of mobile money and access to finance as a constraint.

Table 1:

Next, we break down each obstacle by firm size and its ranking (ordinal scale comprising of no obstacle, minor obstacle, moderate obstacle, major obstacle, and very severe obstacle). Figure 2 demonstrates the different rankings of obstacles for four of the sixteen categories. Based on the figure, it is clear that different sized firms face varying levels of obstacles. Figures 3-5 follow the same setup and a similar conclusion can be drawn.

Figure 3:

Figure 4:

Figure 6:

Figure 7:

From
these figures a general idea of which constraints are likely to be binding in
Kenya for various firm sizes can be identified. Upon applying our model, we
will be able to get a more specific idea of the extent to which each obstacle
constitutes a constraint. Before moving to that step, we can
get a general idea just form our descriptive analysis.

For
small firms, we expect to see political instability, tax rates, corruption,
practices of competitors in the informal sector, and access to land to be
binding. For medium firms, we observe political instability, access to
electricity, corruption, customs and trade regulations, and practices of
competitors in the informal sector. Large firms consistently rank political
instability, access to electricity, tax rates, customs and trade regulations,
and practices of competitors in the informal sector as obstacles. This also
gives us an idea of which obstacles are unlikely to be binding constraints for
firms. For example, courts are rarely ranked as an obstacle and are unlikely to
be an area determining growth for Kenyan firms.

Conclusion

As
the world becomes increasingly aware of the implications of statistical
analysis, the gap between developed and developing countries can become wider.
This article aims to narrow that gap by providing insight into the binding
constraints in Kenya with the intention to assist nonprofits operating there.
In future posts, an ordered probit model will be applied to the Enterprise
Survey data in an effort to apply a more rigorous method to estimating the
constraints. Additionally, a technical article detailing the use of STATA in
this series will also be published. Particular focus will be given to the use
of the cmp command and the evaluation of valid instrumental variables in the
ordered probit model. All data is courtesy of World Bank Enterprise Survey (http://www.enterprisesurveys.org/data/exploreeconomies/2013/kenya).